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相关概念视频

The Cochlea01:13

The Cochlea

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The cochlea is a coiled structure in the inner ear that contains hair cells—the sensory receptors of the auditory system. Sound waves are transmitted to the cochlea by small bones attached to the eardrum called the ossicles, which vibrate the oval window that leads to the inner ear. This causes fluid in the chambers of the cochlea to move, vibrating the basilar membrane.
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Assessing Body Temperature - Tympanic membrane01:14

Assessing Body Temperature - Tympanic membrane

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Assessing tympanic membrane temperature involves using a tympanic membrane thermometer (TMT). Here is a step-by-step guide:
Step 1: Begin by practicing good hand hygiene to prevent the transmission of microorganisms.
Step 2: Turn on the thermometer and wait until the ready sign appears on the screen to ensure accurate measurement.
Step 3: Slide the probe cover in place to prevent cross-contamination.
Step 4: Instruct the patient to tilt their head to the side for comfort and check for cerumen...
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相关实验视频

Updated: Jan 16, 2026

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
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Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention

Published on: December 20, 2024

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基于人工智能的骨导电值预测,使用空气导电听力测量数据.

Chul Young Yoon1,2, Junhun Lee1,2, Jiwon Kim1,2

  • 1Research Institute of Hearing Enhancement, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea.

Journal of clinical medicine
|September 27, 2025
PubMed
概括

人工智能模型可以预测骨导电值和空气-骨隙状态,仅使用空气导电数据. 这一进步支持人工智能在听力学和远程医疗领域的整合,以改善听力护理的可访问性.

关键词:
空气导通的空气导通方式大数据就是大数据.骨导电 骨导电 骨导电 骨导电深度学习是一种深度学习.数字化表型化是指数字化表型化.与听力相关的疾病.

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Extracting the Cochlea from a Human Temporal Bone: A Cadaveric Protocol
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相关实验视频

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科学领域:

  • 听力学 听力学是指听力学.
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 纯音调听力测量 (PTA) 传统上需要空气导电 (AC) 和骨导电 (BC) 测量.
  • 仅从交流数据中估计BC值和分类空气骨间隙 (ABGs) 可以简化听力学评估.

研究的目的:

  • 评估预测BC值和分类ABG状态的可行性,仅使用来自PTA的AC数据.
  • 为了比较各种机器学习模型对这些预测任务的性能.

主要方法:

  • 利用来自韩国的60,718个PTA记录的大数据集.
  • 训练了五个机器学习模型 (DNN,LSTM,BiLSTM,RF,XGB),使用AC值,年龄和性别作为输入特征.
  • 评估模型性能使用准确度,灵敏度,精度和F1得分,并进行5倍交叉验证和SMOTE.

主要成果:

  • 在预测BC值方面,LSTM和BiLSTM模型表现出卓越的性能,在±5dB范围内达到约60%的精度,在±10dB范围内达到80%的精度.
  • 所有模型都在使用10dB标准对ABG分类表现更好,而不是使用15dB标准.
  • 基于树的模型 (RF,XGB) 产生了最高的分类准确度 (高达0.512) 和精度 (高达0.827).

结论:

  • 机器学习模型可以准确地预测BC值和ABG状态,仅使用AC听力测量数据.
  • 这些发现倡导将人工智能工具整合到临床听力学和远程医疗中,用于远程听力查和诊断.
  • 建议进一步进行临床验证和实施,以提高听力护理服务的可访问性.